Ever wonder how Netflix decides what movies to recommend for you? Or how Amazon recommends books? We can get a feel for how it works by building a simplified recommender of our own!
In this capstone, you will show off your problem solving and Java programming skills by creating recommender systems. You will work with data for movies, including ratings, but the principles involved can easily be adapted to books, restaurants, and more. You will write a program to answer questions about the data, including which items should be recommended to a user based on their ratings of several movies. Given input files on users ratings and movie titles, you will be able to:
1. Read in and parse data into lists and maps;
2. Calculate average ratings;
3. Calculate how similar a given rater is to another user based on ratings; and
4. Recommend movies to a given user based on ratings.
5. Display recommended movies for a given user on a webpage.

À partir de la leçon

Interfaces, Filters, Database

In your third step, you will be encouraged to use interfaces to rewrite your existing code, making it more flexible and more efficient. You will also add filters to select a desired subset of movies that you want to recommend, such as 'all movies under two hours long' or 'all movies made in 2012'. You'll also make your recommendation engine more efficient as you practice software design principles such as refactoring.